Motivated by the dense connection mechanism, Tong et al. introduced it into SISR and proposed the SRDenseNet [1]}. SRDenseNet not only uses the layer-level dense connections, but also the block-level one, where the output of each dense block is connected by dense connections. In this way, the low-level features and high-level features are combined and fully used to conduct the reconstruction. In RDN [2]}, dense connections are combined with the residual learning to form the residual dense block (RDB), which allows low-frequency features to be bypassed through multiple skip connections, making the main branch focusing on learning high-frequency information. Apart from aforementioned models, dense connection is also applied in MemNet [3]}, RPMNet [4]}, MFNet [5]}, etc. With the help of dense connection mechanism, the information flow among different depths of the network can be fully used, thus provides better reconstruction results.
